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Date: Tuesday, 26-Nov-96 00:06:57 GMT
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<TITLE>CLINICAL DECISION MAKING GROUP</TITLE>
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<td> <!WA3><a href="http://www.medg.lcs.mit.edu/people/psz/psz.html"><address><b>Peter Szolovits</b></a>,<br>Professor of Computer Science and Engineering</address>
	<!WA4><a href="http://www.medg.lcs.mit.edu/people/doyle/doyle.html"> <address><b>Jon Doyle</b></a>,<br>Principal Research Scientist</address>
	<!WA5><a href="http://www.medg.lcs.mit.edu/people/wjl/vita.html"> <address><b>William J. Long</b></a>,<br>Principal Research Scientist</address></td>
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Computer researchers have long sought to understand the
process of medical decision-making and to use artificial
intelligence (AI) methods to reproduce it in computer
programs. Such programs could help prevent cognitive errors
in health care, bring medical expertise to underserved
parts of the world, and improve medical education and
training. Accordingly, the Laboratory's Clinical Decision
Making Group seeks to learn how doctors think about the
diagnosis and treatment of various medical conditions, and
how their approaches may be captured by AI methods.
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The greatest challenge in building computer programs
designed to mimic medical reasoning is that they must deal
with many complexities. Diseases may appear in unusual
combinations, for example, and exhibit variable symptoms.
Medical care also is rife with basic uncertainties because
of our incomplete understanding of disease and because of
normal variations among patients. Further complicating the
issues, AI programs must reflect individual patient values
and preferences regarding decisions about potentially risky
diagnostics and treatments.
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Working with physicians from nearby institutions (including
New England Medical Center and Children's Hospital), we are
exploring the ways in which medical knowledge is
represented, and how disease may be explained as
physiological "derangements" of the healthy individual.
Theoretical efforts develop general techniques for
representing physiological, anatomical, and biochemical
knowledge; exploring the consequences of disease
pathologies; evaluating time-dependent data; and eliciting
and representing patient preferences. Empirical studies
involve implementation of computer programs that solve
specific medical challenges. Evaluation of the capabilities
and usefulness of such programs helps lead to a better
understanding of future research needs.
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Specific projects within the Group are selected based on
the clinical importance of various disorders and on their
usefulness in designing new AI-related technologies.
Currently we are working on applications to <!WA6><A
HREF="http://www.medg.lcs.mit.edu/projects/hf.html">congestive
heart failure</a>, <!WA7><A HREF="http://www.medg.lcs.mit.edu/projects/geninfer.html">genetic
counseling</a>, the detection of abnormal growth in children, and monitoring treatment in
the intensive care unit (ICU). Earlier studies include the
diagnosis and treatment of coronary artery disease, fluid
and electrolyte disorders, and Hodgkin's disease (a
sometimes fatal disease of the lymphatic system). We also
have studied the design of clinical trial protocols for
lung cancer therapies; problems in administering digitalis;
diagnosis of the causes of acute renal failure and other
kidney disorders, and the management of ventricular
arrhythmias (irregular heart function) in the cardiac ICU.

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